[sergioisidoro]

Yes, in the last example you can see it as rigging the input data. I replicated the results of the experiment in the paper, and I made some remarks of this sorts in my own report and discussion.

The idea is that a sensitive parameter should not contribute to a decision, so, for example, the probability of group A or group B having access to a loan should be the same:

P(Loan|A) should be the same as P(Loan|B)

This is dangerous, as the discrimination metric is sensitive to biases present in the data. It can, effectively, make it easier for the discriminated group A to get a Loan in comparison to a person in group B in same situations. This happens if the bias is not in the annotations, but in the demographics of the dataset.

This is a really interesting problem, and I don't have answers for it.